Acoustic detection for respiratory treatment apparatus

ABSTRACT

Methods and apparatus provide acoustic detection for automated devices such as respiratory treatment apparatus. In some embodiments of the technology, acoustic analysis of noise or sound pulses, such as a cepstrum analysis, based on signals of a sound sensor ( 104 ) permits detection of obstruction (O) such as within a patient interface, mask or respiratory conduit ( 108 ) or within patient respiratory system. Some embodiments further permit detection of accessories such as an identification thereof or a condition of use thereof, such as a leak. Still further embodiments of the technology permit the detection of a patient or user who is intended to use the automated device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/148,730 filed on Aug. 10, 2011, which is a national phase entry under35 U.S.C. § 371 of International Application No. PCT/AU2010/000140 filedFeb. 10, 2010, published in English, which claims priority from U.S.Provisional Patent Application No. 61/253,172 filed Oct. 20, 2009, U.S.Provisional Patent Application No. 61/233,554 filed Aug. 13, 2009, andAustralian Provisional Patent Application No. 2009900561, filed Feb. 11,2009, all of which are incorporated herein by reference.

FIELD OF THE TECHNOLOGY

The present technology relates to methods and apparatus for acousticdetection that may be useful for automated devices such as respiratorytreatment apparatus. Embodiments of the technology may involve detectionof obstruction such as within a patient interface or patient respiratorysystem, detection of accessories or condition thereof, such as a mask,and detection of a patient or user.

BACKGROUND OF THE TECHNOLOGY

Respiratory treatment apparatus, such as a ventilator or positivepressure treatment device, may typically include a flow generator, anair filter, a mask, cannula or endotracheal tube, a supply tubeconnecting the flow generator to the mask or tube, sensors and amicroprocessor-based controller. The flow generator may be aservo-controlled motor and an impeller (e.g., a blower). Optionally, theflow generator may also include a valve capable of discharging air toatmosphere as a means for altering the pressure delivered to the patientas an alternative to motor speed control of a blower. The sensorsmeasure, amongst other things, motor speed, gas volumetric flow rate andoutlet pressure, such as with a pressure transducer, flow sensor or thelike.

Such devices have been automated for making changes to the system. Forexample, CPAP devices have been implemented to detect a condition of thepatient. In U.S. Pat. No. 5,704,345 to Berthon-Jones, a device isdescribed which automatically adjusts treatment pressure in response toindications of partial or complete upper airway obstruction. Anautomated procedure disclosed by Berthon-Jones involves detecting anopen or closed patient airway by inducing airflow with a CPAP pressuregenerator that produces a modulated pressure output. The air flowinduced by the pressure modulation is separated from air flow induced byother factors (such as heartbeat), by demodulating the measured air flowsignal. Apneas are classified as “airway open” if the mean inducedsignal is more then 0.03 l/sec, and “airway closed” if the mean inducedsignal is less than 0.03 l/sec.

In the Patent Cooperation Treaty Published Patent Application No.WO2006/092001, Kwok describes a system of identifying masks by the useof a coded series of resistors. The controller may detect the mask byidentifying a particular electrical resistance based on the resistorsthat is associated with the identity of particular masks.

It may be desirable for improved techniques and devices for assessingthe state of such systems or controlling the operations thereof.

BRIEF SUMMARY OF THE TECHNOLOGY

An aspect of the present technology is to implement acoustic detectionfor various purposes.

Another aspect of the present technology is to implement acousticdetection in or with devices capable of emitting sound or noise.

Another aspect of the present technology is to implement acousticdetection in or with respiratory treatment apparatus.

Another aspect of the present technology is to implement acousticdetection by, for example, cepstrum analysis.

Still further aspects of the present technology are to implementobstruction detection, component or accessory detection and/or patientor user detection by acoustic analysis.

A. Obstruction Detection

One aspect of certain example embodiments of the present technology isto automate a detection of obstruction.

Another aspect of certain example embodiments of the present technologyis to automate a detection of obstruction within a respiratory apparatusconduit.

Another aspect of some embodiments of the present technology includemethods that detect a respiratory treatment conduit obstruction bydetermining with a sound sensor a measure of sound of a flow generatorwithin a respiratory treatment conduit, such as an endotracheal tube.The measure of sound may then be analyzed with a processor. Theprocessor may then indicate a presence or absence of obstruction in therespiratory treatment conduit based on the analyzing.

In some embodiments, the analysis may involve calculating a Fouriertransform from data samples representing the measure of sound. This mayfurther involve calculating a logarithm of the transformed data samplesrepresenting the measure of sound. Still further, the analyzing mayinclude calculating an inverse transform of the logarithm of thetransformed data samples representing the measure of sound. In someembodiments, the analyzing may also involve calculating a differencebetween (a) the inverse transform of the logarithm of the transformeddata samples representing the measure of sound and (b) an inversetransform of a logarithm of Fourier transformed data samplesrepresenting a sound measured from an unobstructed version of therespiratory treatment conduit.

In still further embodiments, the indicating may involve displaying agraph of data on a display based on the difference calculating.Moreover, a location and extent of the presence of obstruction based ondata of the difference calculating may be determined. Such an extent mayoptionally be determined from an amplitude or magnitude value of asignificant sample of the data of the difference calculating. Thelocation may be determined from a time of the significant sample in thedata of the difference calculating.

Optionally, the sound source that generates the sound within therespiratory treatment conduit may be a flow generator. Still further,the sound sensor may be a single microphone.

Some embodiments of the technology also involve an apparatus fordetecting a respiratory treatment conduit obstruction. The apparatus mayinclude a microphone adapted for coupling with a respiratory treatmentconduit to generate a measure of sound of a flow generator within therespiratory treatment conduit. A processor of the apparatus may beconfigured to analyze data samples of the measure of sound from themicrophone and to indicate a presence or absence of obstruction in therespiratory treatment conduit based on the analyzed data samples.Optionally, the microphone may be adapted with an endotracheal tubecoupler having an opening to connect with a portion of an endotrachealtube. The endotracheal tube coupler may also include an opening adaptedto connect with a portion of a ventilator supply tube. The coupler mayfurther include a microphone chamber adapted with a membrane to separatethe chamber from a gas channel of the coupler. Optionally, the couplermay further include a vent adapted to permit the microphone chamber toequalize with ambient pressure. In some embodiments, the apparatus mayalso include a flow generator and the processor may be configured tocontrol the flow generator to generate a respiratory treatment.Similarly, the processor may be configured to determine obstruction inaccordance with any of the previously described methods.

In some embodiments of the technology, a method implements detecting ofrespiratory system obstruction. The method may include determining witha sound sensor a measure of sound of a flow generator. A processor maythen analyze the measure of sound from the sound sensor by calculationof a cepstrum from the measure of sound. The processor may then indicatea presence or absence of obstruction (e.g., partial or full) in therespiratory system of a patient based on the analyzing. In someembodiments, this may involve detecting an extent of the presence ofobstruction based on calculating a difference between the cepstrum fromthe measure of sound and a cepstrum determined from a prior measure ofsound. Optionally, the extent may be determined from an amplitude valueof a significant sample of the data of the difference calculating.Moreover, a location may be determined from a position of thesignificant sample in the data of the difference calculating, such thatthe position represents a point beyond a known end of a respiratorytreatment conduit.

Such methods may be implemented in an apparatus for detecting arespiratory obstruction. For example, the apparatus may include amicrophone adapted for coupling with a respiratory treatment conduit togenerate a measure of sound of a flow generator. The apparatus may alsoinclude a controller or processor configured to analyze data samples ofthe measure of sound from the microphone by calculation of a cepstrumwith the data samples of the measure of sound. The apparatus may befurther configured to indicate a presence or absence of obstruction inthe respiratory system of a patient based on the analyzed data samples.The apparatus may also optionally include a flow generator, where thecontroller or processor is further adapted to control the flow generatorto generate a respiratory treatment.

B. Accessory Detection

One aspect of the technology is directed towards a recognition systemthat provides structure to facilitate the coordination between the flowgenerator and the peripheral components.

Another aspect of the present technology is to provide methods andapparatus for automatic detection of the type of mask connected to aCPAP device. Further aspects of the present technology may include:methods, systems and devices to detect and/or identify characteristicsin the airpath of CPAP device including patient interfaces, andpatient's respiratory systems.

Another aspect of the technology relates to an adapter for use with aflow generator that generates a supply of pressurized air to be providedat an outlet to a patient for treatment. The adapter includes a conduitattachable to the outlet of the flow generator, and an identifyingelement supported by the conduit and providing an identifying featureunique to a specific peripheral component attachable to the flowgenerator. The identifying feature is communicable to the flow generatorso that appropriate operating parameters of the flow generator may beautomatically selected by the flow generator to coordinate with thespecific peripheral component.

C. Patient/User Detection

An aspect of certain example embodiments of the present technology is toautomate a method of detection or authentication of a user of a device.

Another aspect of certain example embodiments of the present technologyis to automate a detection of particular user of a device so as topreclude or permit operations(s) in accordance with the detected user.

Another aspect of certain example embodiments of the present technologyis to automate a detection of particular user of a respiratory treatmentapparatus as a safety feature.

A still further aspect of example embodiments of the technology is amethod for authenticating a user of a device including determining witha sound sensor a measure of sound of a sound generator within a soundconduit directed to an anatomical cavity of a user of a device. Themethod may further involve analyzing the measure of sound from the soundsensor with a processor by calculation of a cepstrum from the measure ofsound. The method may still further involve determining with theprocessor that the user is a pre-authorized user based on the analyzing.

In some embodiments, the determining in the method may includepermitting an operation of the device. In some embodiments, the soundgenerator comprises a speaker and the sound sensor comprises amicrophone. In some embodiments, the device protected by theauthentication is a respiratory treatment apparatus where the soundgenerator includes a flow generator and the sound conduit includes arespiratory supply tube. In some cases, the analyzing may involvecomparing data of the cepstrum with data of a prior cepstrum determinedfrom a prior measure of sound in a setup process. In some such cases,the method may include determining a prior measure of sound taken in asetup process.

In some embodiments, the technology may include an apparatus forauthenticating a user. The apparatus may include a sound conduit adaptedto direct an acoustic signal to an anatomical cavity of a user of theapparatus. The apparatus may also include a sound generator to generatethe acoustic signal. The apparatus may further include a microphoneadapted for coupling with the sound conduit to generate a measure of theacoustic signal. The apparatus may also include a processor configuredto analyze data samples of the measure of the acoustic signal from themicrophone by calculation of a cepstrum with the data samples of themeasure of sound. The processor may also be configured to determine thatthe user is a pre-authorized user based on the analysis.

In some of the embodiments, the processor may be configured to permit anoperation of the apparatus based on the determination. The soundgenerator of the apparatus may include at least one speaker. Theprocessor of the apparatus may also optionally conduct the analysis bycomparing data of the cepstrum with data of a prior cepstrum determinedfrom a prior measure of sound taken in a setup process. In someembodiments, the processor of the apparatus may also be configured toset the prior measure in a setup process. In some embodiments, theapparatus may be a respiratory treatment apparatus where the soundgenerator is a servo-controlled blower and the sound conduit comprises arespiratory supply tube and mask or nasal cannula.

Other features of the technology will be apparent from consideration ofthe information contained in the following detailed description,drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present technology is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings, in whichlike reference numerals refer to similar elements including:

FIG. 1 illustrates example components of a system for detectingrespiratory treatment conduit obstruction of the present technology;

FIG. 2 is an example methodology for a device that may implementrespiratory treatment conduit obstruction detection of the presenttechnology;

FIG. 3 is a graph of cepstrum data from two sound waves measured from anunobstructed respiratory treatment conduit in accordance with someembodiments of the technology;

FIG. 4 is a graph of the difference of the cepstrum data of the twosound waves of FIG. 3;

FIG. 5 is a graph of cepstrum data from two sound waves taken from acommon respiratory treatment conduit with and without obstructiondetermined in accordance with some embodiments of the technology;

FIG. 6 is a graph of a magnitude of the difference in the cepstrum datafrom the two sound waves of FIG. 5;

FIG. 7 is a cross sectional view of an embodiment of a sound sensor in aconduit embodiment of the present technology;

FIG. 8 is an perspective view of an embodiment of a respiratorytreatment conduit coupler with a sound sensor of the present technology;

FIG. 9 is an perspective view of the embodiment of the coupler of FIG. 8fitted with an endotrachael tube;

FIG. 10 is an perspective view of the embodiment of the coupler of FIG.9 also fitted with a flow generator supply conduit;

FIG. 11 is an illustration of example components of a respiratorytreatment apparatus configured with obstruction detection in accordancewith some embodiments of the present technology;

FIG. 12 illustrates a block diagram of an example controllerarchitecture of the present technology with conduit obstructiondetection technology;

FIG. 13 is a schematic view of a first preferred embodiment of thepresent invention;

FIG. 14 is a graph demonstrating an example of an Impulse ResponseFunction as per the first preferred embodiment;

FIG. 15 is a further graph demonstrating various cepstra of variousexample masks at various flow generator speeds;

FIG. 16 depict a first example of a mask for use with an exampleembodiment;

FIG. 17 depict a first example of a mask for use with the an exampleembodiment;

FIG. 18 depict a first example of a mask for use with the an exampleembodiment;

FIG. 19 illustrates example components of a system or apparatus forauthenticating or detecting a particular user of a device of the presenttechnology;

FIG. 20 is an example methodology for user detection of a device of thepresent technology;

FIG. 21 is a graph of hypothetical cepstrum data from two sound wavesmeasured from a common user at different times;

FIG. 22 is a graph of the difference of the cepstrum data from the twosound waves of FIG. 21;

FIG. 23 is a graph of hypothetical cepstrum data from two sound wavestaken from two different users;

FIG. 24 is a graph of a magnitude of the difference in the cepstrum datafrom the two sound waves of FIG. 23;

FIG. 25 is an illustration of components of an example respiratorytreatment apparatus configured with authentication technology inaccordance with some embodiments of the present technology; and

FIG. 26 illustrates a block diagram of an example controllerarchitecture of the present technology with user detection technology.

DETAILED DESCRIPTION

Some embodiments of the present acoustic detection technologies mayimplement cepstrum analysis. A cepstrum may be considered the inverseFourier Transform of the log spectrum or the forward Fourier Transformof the decibel spectrum, etc. The operation essentially can convert aconvolution of an impulse response function IRF and a noise or soundsource into an addition operation so that the noise or sound source maythen be more easily accounted for or removed so as to isolate data ofthe system impulse response function for analysis. Techniques ofcepstrum analysis are described in detail in a scientific paper entitled“The Cepstrum: A Guide to Processing” (Childers et al, Proceedings ofthe IEEE, Vol. 65, No. 10, October 1977) and RANDALL RB, FrequencyAnalysis, Copenhagen: Bruel & Kjaer, p. 344 (1977, revised ed. 1987).Other references describing cepstum analysis may be available.

Such a method may be understood in terms of the property of convolution.The convolution of f and g can be written as f*g. This operation may bethe integral of the product of the two functions (f and g) after one isreversed and shifted. As such, it is a type of integral transform asfollows:(f*g)(t)

∫_(−∞) ^(∞) f(τ)·g(t−τ)dτ

While the symbol t is used above, it need not represent the time domain.But in that context, the convolution formula can be described as aweighted average of the function ƒ(τ) at the moment t where theweighting is given by g(−τ) simply shifted by amount t. As t changes,the weighting function emphasizes different parts of the input function.

More generally, if f and g are complex-valued functions on R^(d), thentheir convolution may be defined as the integral(f*g)(x)=∫_(R) _(d) f(y)g(x−y)dy=∫ _(R) _(d) f(x−y)g(y)dy.

A mathematical model that can relate an acoustic system output to theinput for a time-invariant linear system, such as one involving conduitsof a respiratory treatment apparatus, (which may include some human orother unknown part of the system) can be based on this convolution. Theoutput measured at a microphone of the system may be considered as theinput noise “convolved” with the system Impulse Response Function (IRF)as a function of time (t).y(t)=s ₁(t)*h ₁(t)  Equation 1Where:

* denotes the convolution function.

y(t) is the signal measured at the microphone.

s₁(t) is the sound or noise source such as a noise or sound created inor by a flow generator (“FG”) of a respiratory treatment apparatus.

h₁(t) is the system IRF from the noise or sound source to themicrophone.

The Impulse Response Function (IRF) is the system response to a unitimpulse input.

Conversion of equation 1 into the frequency domain by means of theFourier Transform of the measured sound data (e.g., a discrete FourierTransform (“DFT”) or a fast Fourier transform (“FFT”)) and consideringthe Convolution Theorem, the following equation is produced:

$\begin{matrix}{{y(t)} = {{{s_{1}(t)}*{{h_{1}(t)}\overset{FourierTrasform}{\longrightarrow}{Y(f)}}} = {{S_{1}(f)} \cdot {H_{1}(f)}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$Where:

Y(f) is the Fourier Transform of y(t);

S₁(f) is the Fourier Transform of s₁(t); and

H₁(f) is the Fourier Transform of h₁(t).

In such a case, convolution in the time domain becomes a multiplicationin the frequency domain.

A logarithm of equation 2 may be applied so that the multiplication isconverted into an addition:

$\begin{matrix}{{{Log}\left\{ {Y(f)} \right\}} = {{{Log}\left\{ {{S_{1}(f)} \cdot {H_{1}(f)}} \right\}} = {{{Log}\left\{ {S_{1}(f)} \right\}} + {{Log}\left\{ {H_{1}(f)} \right\}}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

Equation 3 may then be converted back into the time domain, by anInverse Fourier Transform (IFT) (e.g., an inverse DFT or inverse FFT),which results in a Complex Cepstrum (K(τ)) (Complex because we may workfrom the complex spectrum)—the inverse Fourier Transform of thelogarithm of the spectrum.K(τ)=IFT[Log{S ₁(f)}+Log{H ₁(f)}]  Equation4

“τ” is a real valued variable known as quefrency, with units measured inseconds. So we can see that effects that are convolutive in the timedomain become additive in the logarithm of the spectrum, and remain soin the cepstrum.

Consideration of the data from a cepstrum analysis, such as examiningthe data values of the quefrency, may provide information about thesystem. For example, by comparing cepstrum data of a system from a prioror known baseline of cepstrum data for the system, the comparison, suchas a difference, can be used to recognize differences or similarities inthe system that may then be used to implement automated control forvarying functions or purposes.

The following example embodiments may utilize the methodologies of suchan analysis as herein explained to implement different detectors usefulfor various purposes.

A. Obstruction Detection

Invasively ventilated patients may be treated with ventilator devices bytracheal intubation. A flexible endotracheal tube is inserted into thetrachea of the patient. The tube may then be connected to therespiratory treatment apparatus, which may optionally be a mechanicalventilator. The tube directs a flow of ventilatory support from theventilator to the lungs to enable patient ventilation. The tube ensuresthat the patient's airways remain open or unobstructed for the deliveryof the support. However, in some cases, bio-material such as mucus maybuild up on the inside of the tube. The presence of a significant amountof such material can cause extra pneumatic resistance in the tube.

If the resistance caused by this material becomes too great, it caninterfere with ventilation support. In such cases, the patient must beextubated so that the tube may either be replaced or cleaned. In somecases, an endotracheal camera may be utilized to inspect the interior ofthe tube and monitor the progression of any buildup in the tube.However, this is not an easy procedure and such a camera can be costly.

Thus, embodiments of the present technology may involve methods anddevices for the detection of obstruction within respiratory treatmentapparatus conduits such as an endotracheal tube or supply tube and mask.As illustrated in FIG. 1, the respiratory treatment conduit obstructiondetection apparatus 102 may be implemented to detect the presence ofobstruction (e.g., partial or otherwise), such as mucus or otherbio-substance, within a conduit by sound wave measurement and analysis.Such a detector apparatus will typically include a sound sensor 104,such as a microphone, and a detection controller 106.

In a typical embodiment, the sound sensor 104 measures sound traversingwithin a respiratory treatment conduit 108 for analysis by the detectioncontroller 106. For example, the sound may be that generated by a soundsource. The sound may be the vibrations or sound created by theoperation of a flow generator 110 such as a servo-controlled blower. Forexample, the flow generator may be supplying a flow of breathable gasvia an optional supply conduit 112 to the respiratory treatment conduit108. In the case where the respiratory treatment conduit 108 is withinthe respiratory system of the patient, the breathable gas may therebyprovide a respiratory treatment to the patient. This measured sound mayinclude sound waves reflected from an obstruction (illustrated byreference character “O” in FIG. 1) in the respiratory treatment conduitwhen present.

A sound signal from the sound sensor 104 can be sent to the detectioncontroller 106. Optional analog-to-digital (A/D) converters/samplers(not shown separately) may be utilized in the event that supplied signalfrom the sensor is not in digital form and the controller is a digitalcontroller. Based on the signal from the sensor, the controller assessesthe sound signal to determine obstruction data, such as the presence orabsence of obstruction, an extent of obstruction or a position of theobstruction in the respiratory treatment conduit.

In some embodiments, the detection controller 106 may include aprocessor configured to implement particular detection methodologiessuch as the algorithms described in more detail herein. Thus, thecontroller may include integrated chips, a memory and/or other controlinstruction, data or information storage medium. For example, programmedinstructions encompassing such a detection methodology may be coded onintegrated chips in the memory of the device. Such instructions may alsoor alternatively be loaded as software or firmware using an appropriatedata storage medium. With such a controller or processor, the device canbe used for determining and analyzing sound data from the sound sensor.Thus, the processor may control the assessment of obstruction asdescribed in the embodiments discussed in more detail herein.

One example of such a methodology or algorithm of the controller 106 ofthe respiratory treatment conduit obstruction detection apparatus 102 isillustrated in the flow chart of FIG. 2. At 220, a sound sensor measuressound of a flow generator within a respiratory treatment conduit, suchas under the control of the controller. At 222, the measure of soundfrom the sound sensor is analyzed by the controller or a processorthereof. At 222, the controller or processor indicates a presence orabsence of obstruction in the respiratory treatment conduit based on theanalyzing.

In such an embodiment of the technology, there may be a number of designconsiderations relating to the acoustic nature of the system. Thepatient ventilator circuit can have many different variations in termsof the combination and permutation of components. Each combination mayhave different acoustic properties. One common component to thesecombinations may be a respiratory treatment conduit 108, such as anendotracheal tube, with an approximately constant cross section alongits length “L” (when it is not obstructed), with the patient at apatient end of the conduit (shown as “PE” in FIG. 1), and the ventilatorcircuit (with other potential components) at the flow generator end(shown as “FGE” in FIG. 1). An acoustic characteristic of the conduit(when not obstructed) may be that it acts as a wave guide for a widerange of frequencies along its length. Thus, there may be no significantchange in an acoustic signal as it propagates down the endotracheal tubeother than the time delay associated with its propagation speed.

Another factor to consider may be that the flow generator or ventilatorand patient can both be sources of random, cyclostationary, anddeterministic noise. In addition, flow induced noise can be the resultof the structural design of the components where there is flow in thesystem (e.g., supply conduits, etc.).

Thus, in some embodiments of the apparatus, detecting a change may bebased on the acoustic reflection in a conduit (or endotracheal tube)during flow generator operations as the conduit becomes increasinglyobstructed by a build up of material on its internal walls by comparisonto when it was unobstructed. When the tube is clean or new, it can befree from obstruction. The Impulse Response Function (“IRF”) of thesound or noise in such a system created by a flow generator of aventilator circuit serving as a sound source to a sound sensor, cancontain (or be considered) a delta function at a chosen time zero, and areflection from the patient end PE of the tube at time 2L/c, where thespeed of sound is denoted by “c”, and the length of the conduit orendotracheal tube is “L”. Thereafter, if the tube becomes partiallyobstructed by an obstruction O along its length, another IRF of thesystem may now contain a new reflection from the obstruction, at time2x/c, where “x” is the distance from the sound sensor to theobstruction.

One potential method for monitoring for such a change in the reflectionfrom the conduit or endotracheal tube may be based on the calculation ofa cepstrum of a signal from the sound sensor. By comparing cepstrum datafrom a known clean conduit with cepstrum data from a potentiallyobstructed conduit, the comparison, such as differences there between,may be considered in identifying obstruction in the conduit. Forexample, if a common noise source is used from both tests, such as thesame type of ventilator circuit or flow generator operating at the samesettings (e.g., pressure delivery, speed and/or flow etc.), thecomparison or difference between the cepstrum of the unobstructed tubeand the cepstrum of the obstructed tube may be implemented to indicate,for example, (a) the existence of obstruction of the conduit, (b)location of the obstruction in the conduit, and/or (c) an extent of theobstruction, such as by consideration of an amplitude or magnitude ofthe difference data.

The Impulse Response Function (IRF) is the system response to a unitimpulse input. Some factors of the IRF (from the flow generator as asound source to the microphone response) are herein explained. When theconduit acts as a wave guide for sound produced by the flow generator.Sound is emitted and forms a first signal (illustrated in FIG. 1 as“A1”). The sound or first signal travels down or along the conduit tothe end or obstruction and is reflected back along the conduit. Thereflected sound may be considered a second signal A2 (illustrated inFIG. 1 as “A2”). As previously mentioned, a feature of the conduitresponse is the time required by sound to travel from one end of thesystem to the opposed end. This delay may mean that the sound sensorpositioned at one end of the conduit receives the first signal comingfrom the flow generator, and then some time later receives the samesound filtered by the conduit as reflected second signal A2 (andpotentially any other system attached, like human respiratory system,when the conduit is intubated within a patient). This may mean that thepart of the IRF associated with the reflection from the conduit appearsafter a delay. The delay may be considered approximately equal to thetime taken for sound to travel from the sound source to the patient endor to an obstruction of the conduit, be reflected, and travel backagain.

When the system is loss-prone, given the length of the conduit, the partof the IRF associated with the response at the flow generator will decayto a negligible amount by the time the reflection response has begun.When this occurs, the response due to obstruction may be completelyseparated from the flow generator response in the system IRF.

For example, a generated noise or noise source can be produced by a flowgenerator running at a constant speed during the time period of thesound sensor's measurement. This noise may be described as“cyclostationary”. That is, it is stationary random, and periodic in itsstatistics. This means that the noise source and system response may be“smeared” across all measured times because at any point in time, thesystem output is a function of all previous values of the input signaland system response.

A potential methodology and system for separating the obstructionreflection in some embodiments from this convolutive mixture of soundmay be performed in accordance with the operations described above withregard to Equations 1, 2, 3 and 4.

The separation of the obstruction reflection may be assisted by the factthat the cepstrum of white noise (a signal with a flat spectrum) isshort such that it appears only at the beginning of the cepstrum, but ashas already been shown, the part of the system IRF containing theobstruction reflection appears after the time delay caused by the tube.This can mean that if the flow generator noise is white enough theobstruction reflection response will be separated from both the noiseand response of the flow generator.

Thus, in some embodiments, a continuous sound of the operation of theflow generator may be taken as the sound impulse (s₁(t)) to the systemby considering an arbitrary point in time during the continuous sound tobe the sound impulse. In such a case, the sound generator would not needto produce periods of silence or reduced sound before and after arelative increase in sound to thereby produce an actual momentary soundimpulse. However, in some embodiments such a momentary sound impulse maybe generated by modulation of the control signals to the flow generatoror sound source such as by setting low or no speed, followed by aninstantaneous high speed and then followed by a return to the low or nospeed. Other methods of implementing a momentary sound impulse may alsobe implemented. For example, a speaker may be used to generate anacoustic sound pulse or chirp. Such a sound pulse may even be a broadspectrum impulse.

FIGS. 3 through 6 show graphs illustrating an analysis of sound datafrom a microphone to detect a presence of obstruction based on thecepstrum methodology as previously described. In FIG. 3, data from twodistinct sound measurement tests are plotted on a common axis. Sound canbe measured such that samples of the microphone signal may be collectedor recorded from a chosen or controlled time zero until a sufficientperiod of time has lapsed to permit the sound to traverse the conduit tothe patient end and return to the microphone. In both cases illustratedin FIG. 3, the conduit subject to the measurement process wasunobstructed. The measurement samples or sound data from the microphonein each test was subjected to the operations described by equations 1,2, 3 and 4 previously mentioned and then plotted. In FIG. 4, thedifference or magnitude of the difference from the data of the twoplots. Such a difference or magnitude may optionally be determined on asample-by-sample basis as the absolute value of the difference betweenthe sound data of the two tests. The approximately flat line having nosignificant samples may be taken as a representation of an absence ofobstruction along the conduit.

In FIG. 4, data from two distinct sound measurement tests are againplotted on a common axis. In one case the conduit subject to themeasurement process was unobstructed and in the other case the conduitof the measurement process was obstructed. The sound data from themicrophone in each test was subjected to the operations described byequations 1, 2, 3 and 4 previously mentioned and then plotted. In FIG.5, the difference or magnitude of the difference from the data of thetwo plots was then determined on a sample-by-sample basis and plotted.The presence of any significant difference in one or more samples (e.g.,one or more values in excess of a threshold at a point along the plot)may be representative of a presence of obstruction or obstructions alongthe conduit. Such a determination may be made by scanning and assessingthe samples of the difference data. Moreover, the magnitude of the valueof a particular difference point along the plot of the data may berepresentative of the extent of the obstruction. Still further, theposition of a point along the plot may be assessed as a position ofobstruction along the conduit given that the cepstrum data is a functionof seconds as follows:Length from sound sensor=(T _(s) ×C)/2Where:

T_(s) is a time position of a significant sample in seconds; and

C is the speed of sound.

It will be understood that this calculation may be adjusted to accountfor the distance from the microphone to the ventilator end of the testedconduit or endotracheal tube.

In an apparatus configured with such a methodology, a pre-measuringprocess may optionally be performed by the apparatus with a knownnon-obstructed tube when it is first connected to the apparatus beforeor at initial use with a patient. Alternatively, data from such aprocess may be pre-stored based on standard equipment configurations andoperational settings. The pre-stored data may then be selected by theuser of the apparatus for comparison with new test data. Then, whensubsequent tests are made during patient treatment by the apparatus atcommon ventilator operation settings as the pre-measuring process, thesubsequent test data may be used for comparison with the prior data todetect the obstruction and generate associated obstruction information.

While a simple graph like the ones displayed in FIGS. 3 to 6 may begenerated by the apparatus to indicate obstruction information, in someembodiments, more detailed reports of the obstruction information may beoutput to a display device of a detection apparatus or electronicallytransferred to another apparatus for display on the other apparatus(e.g., a computer or respiratory treatment apparatus). For example, thereport may include information identifying (1) whether or notobstruction exists in the endotracheal tube, (2) where an obstruction islocated in units of distance from either the microphone, ventilator endand/or the patient end of the endotracheal tube, (3) an extent ofobstruction such as a percentage or other measure of the cross sectionthat is blocked by the obstruction or still remaining open. The detectormay even trigger a warning message and/or alarm in the event of thedetection of a substantial obstruction, such that it may recommend orwarn of the need of replacement or cleaning of a current endotrachealtube in use. Thus, a controller of such an apparatus may optionallyinclude a display device such as one or more warning lights (e.g., oneor more light emitting diodes). The display device may also beimplemented as a display screen such as an LCD. Activation of thedetector such as to initiate a pre-measuring process, selectpre-measured tube data, initiate an obstruction measuring process, etc.may be performed in conjunction with a user interface such as inputswitches that operate the controller or processor of the detectionapparatus.

In some embodiments of the technology, the sound sensor may beintegrated with a respiratory treatment apparatus conduit (e.g., anendotracheal tube or ventilator supply conduit) or implemented as partof a conduit coupler between conduits. For example, a microphone may beimplemented as illustrated in FIGS. 7 and 8. FIG. 7 shows a crosssectional view of a sound sensor integrated into a conduit or coupler.In this example, the microphone 772 is installed into a microphonechamber 774 that is formed within a wall 770. The chamber facilitatesthe capture of sound from an internal gas flow channel of the conduit.In the embodiment, the wall 770 may serve as a barrier for the flow ofgas within the channel of the conduit or coupler. Optionally, a chamberbarrier, such as a sound conducting membrane, may separate the gaschannel of the conduit and the sound sensor. Such a barrier may serve toprotect the microphone. As further illustrated in FIG. 7, the wall mayalso include a vent 778 adapted to permit the microphone chamber toequalize with ambient pressure. Optionally, the channel of the conduitmay include a bevel 780 surface, such as the one illustrated with theconic cross-section shown in FIG. 7. Such a surface may improve acousticproperties of the conduit in its use as a wave guide.

FIGS. 8, 9 and 10 illustrate an example conduit coupler 800 aspreviously mentioned for an endotrachael tube. The coupler 800 includesan endotracheal tube mount end 882 and a ventilator supply tube mountend 884. In this embodiment, the ends are sized for connection witheither the ventilator supply tube or the endotracheal tube or suitableadapters for the tubes. For example, the tubes may be held in connectionwith the coupler and/or adapters by interference fit. The sound sensor872 may optionally be integrated or installed within or into the coupleras described with respect to FIG. 7. In FIG. 9, the coupler 800 isoptionally connected with an endotrachael tube 990 via an adapter 992.Such an adapter includes a gas channel to permit gas and sound transferbetween the coupler and the tube. In FIG. 10, the coupler 800 isconnected with a ventilator supply tube 1094.

In reference to the embodiment of FIG. 1, the obstruction detectionapparatus 102 may serve as a detector that is used with, butstructurally independent of, a respiratory treatment apparatus. In suchan embodiment, the common operational settings of the flow generatorused for the tests may be manually set by a clinician. However, in someembodiments, the obstruction detection apparatus 1102 may be integratedwith or be a component of a respiratory treatment apparatus, such as inthe embodiment illustrated in FIG. 11. In such a device, the controller1106 that controls the delivery of pressure or ventilation treatment ofa patient via a flow generator, may also serve as the tube obstructiondetection controller. In such an embodiment, the sound sensor 1104 maybe directly coupled with the controller 1106 of the respiratorytreatment apparatus for the acoustic measurement of obstruction of anendotracheal tube 1108. Such a device may include a pressure sensor,such as a pressure transducer to measure the pressure generated by theblower 1100 and generate a pressure signal p(t) indicative of themeasurements of pressure. It may also optionally include a flow sensor.Based on flow f(t) and pressure p(t) signals, the controller 1106 with aprocessor may generate blower control signals.

For example, the controller may generate a desired pressure set pointand servo-control the blower to meet the set point by comparing the setpoint with the measured condition of the pressure sensor. Thus, thecontroller 404 may make controlled changes to the pressure delivered tothe patient interface by the blower 102. Optionally, it may include aspeed sensor so as to control the blower to a particular RPM setting. Inthis regard, in addition to automated respiratory treatment, theobstruction measuring processes of the apparatus may be automated so asto directly control particular settings of the blower during theacoustic measuring as previously described. In this manner it maymaintain common operational settings during tube obstruction tests.

An example architecture for a conduit obstruction detection controller1206 is illustrated in the block diagram of FIG. 12. In theillustration, the controller may be implemented by one or moreprogrammable processors 1208. The device may also include a displayinterface 1210 to output data for a user interface or display device aspreviously discussed (e.g., detected conduit obstruction information,etc.) to a display such as on a monitor, LCD panel, touch screen, etc. Auser control/input interface 1212, for example, for a keyboard, touchpanel, control buttons, mouse etc. may also be included as previouslydiscussed and for inputting data, or otherwise activating or operatingthe methodologies described herein. The device may also include a sensoror data interface 1214, such as a bus, for receiving/transmitting datasuch as programming instructions, settings data, sound data, microphonesound samples, acoustic measurement data, obstruction information, etc.

The controller also includes memory/data storage components 1220containing control instructions and data of the aforementionedmethodologies. For example, at 1222, they may include stored processorcontrol instructions for sound signal processing and tube obstructioninformation detection, such as, measurement, filtering, FFT, logarithm,position determination, extent determination, difference determinationetc. At 1224, these may also include stored processor controlinstructions for flow generator control, such as respiratory treatmentcontrol based on feedback processing and measuring process settingsadjustment, etc. Finally, they may also include stored data at 1126 forthe methodologies such as sound measurements, detected obstructions,position data, extent data, pre-measurements for unobstructed tubes,reports and graphs, etc.

In some embodiments, the processor control instructions and data forcontrolling the above described methodologies may be contained in acomputer readable recording medium as software for use by a generalpurpose computer so that the general purpose computer may serve as aspecific purpose computer according to any of the methodologiesdiscussed herein upon loading the software into the general purposecomputer.

While the conduit obstruction detection technology has been described inseveral embodiments, it is to be understood that these embodiments aremerely illustrative of the technology. Further modifications may bedevised within the spirit and scope of this description.

For example, while an integrated obstruction measuring and reportingdevice is contemplated by the present technology, the methodology of thecomponents of the device may be shared across multiple components of asystem. For example, a measuring device may simply conduct the measuringprocesses to determine the acoustic data of the conduits and transferthe data to another processing system. The second processing system mayin turn analyze the data to determine the obstruction information aspreviously discussed. The second processing system may then indicate theobstruction as described herein, such as by sending one or more of thedescribed messages, in electronic form for example, back to themeasuring or other apparatus for display to warn the clinician orphysician.

Similarly, while the technology contemplates embodiments where data fromonly a single microphone may be implemented to detect conduitobstruction, in some embodiments of the technology additionalmicrophones may be implemented. Moreover, while the technologycontemplates embodiments where the noise or sound of the system thatserves as the sound impulse is the sound generated by a flow generatoroperating at one or more chosen blower settings, in some embodiments, aspeaker or horn driver may be implemented to generate the sound impulsein the conduit that is recorded by the sound sensor. Moreover, whilesome embodiments are implemented to compare cepstrum data from knownunobstructed tubes with the cepstrum data of obstructed tubes,additional comparisons may be made between tubes with varying degrees ofobstruction so that the changing nature of an obstruction may be trackedand indicated to a user.

In some embodiments, the technology may also be implemented to detect apresence of obstruction of the respiratory pathways of the patient'srespiratory system, such as closure or partial closure (e.g., narrowingassociated with obstructive apnea). For example, based on the cepstrumanalysis and determination of a distance from the microphone aspreviously described, a detection of a significant value in the cepstrumdifference data may be indicative of obstruction or partial obstructionbeyond the length of any respiratory treatment conduit, endotrachealtube or mask. In such a case, the data may be taken as an indication ofobstruction of the patient's respiratory pathways. In such anembodiment, a period of sound data may be recorded so as to collectsufficient data for sound to reflect back from beyond the end of theconduit or mask of the respiratory apparatus. By conducting theaforementioned cepstrum analysis and difference calculation, a devicemay then indicate patient obstruction (in addition to or as analternative to treatment conduit obstruction). Based on one or moresignificant values in the cepstrum difference data that are associatedwith a distance beyond the known end of the apparatus conduit, an extentof obstruction (e.g., increase or decrease), presence or absence ofobstruction and/or a position of obstruction may then be displayed oroutput by the detection apparatus in a similar manner as previouslydescribed with regard to obstruction of a respiratory treatment conduit.In such a device a pre-measuring process may be implemented to determinecepstrum data for the system when it is known that the respiratoryapparatus and patient respiratory system are either unobstructed orotherwise less obstructed so that later analysis of test data may becompared to indicate a change in obstruction (e.g., increase ordecrease).

Such an analysis of acoustic reflection data of the patient'srespiratory system can serve generally as a test to detect the conditionof the patient's respiratory system in addition to a detection ofpresence of absence of obstruction or partial obstruction. For example,acoustic reflection data may be analyzed to detect lung condition and/ormonitor changes in lung condition. For example, an apparatus may beconfigured to measure the acoustic reflection from the lungs overseveral days (e.g., once a day) and then compare the data from each todetect changes. Changes may indicate improvements or deterioration inrespiratory condition. It may even be compared to templates representingempirically collected and stored reflection data that is associated withcertain respiratory related conditions.

In some embodiments where reflection data of particular interest is inthe patient's respiratory system, the acoustic response of the patientinterface (e.g., tube or mask) may be made so as to reduce thepossibility of reflections that result from the mask or conduits. Inthis way, reflection data may be more readily attributable to therespiratory condition of the patient.

In still further embodiments, reflection data may be analyzed to detectlung or patient characteristics such as lung impedance, rhinometry,whether a humidifier is needed, heart failure via odema and otherincreases in patient airway resistance.

In some embodiments, the frequency domain may be processed to isolate oremphasize data of interest in detecting a particular condition or systemaccessory. For example, the process may involve filtering with respectto particular frequencies (e.g., low pass, high pass, band pass, etc.).In this regard, it may be useful to include or exclude certain spectralcomponents such as filtering out spectral components to excludefrequencies not particularly related to a detection of interest. Forexample, frequencies associated with snoring sounds or leak sounds maybe filtered out to assist in mask detection or other patient conditiondetection. In this regard, information about mask geometry mighttypically be contained in higher frequency signal components, whereasinformation about leak and snore and perhaps lung parameters may be morereadily seen in lower frequency components of the signal. By way offurther example, the process might adjust the sound sampling parameterssuch as sample rate and record length to suit the particular detectionapplication of interest. For example in order to detect informationabout a patient's snoring condition (which typically consists ofrelatively low frequencies) it might be advantageous to implementrecording or capturing of sound data during a longer period of timewhile at the same time the sampling rate may be reduced. In such a case,further filtering out of particular frequencies associated with themotor or impeller of the flow generator may be useful.

B. Accessory Detection

As previously mentioned, apparatus to deliver breathable gas to apatient typically includes a flow generator, an air delivery conduit,and a patient interface. A variety of different forms of patientinterface may be used with a given flow generator, for example nasalpillows, nasal mask, nose & mouth mask, full face mask. Furthermore,different forms of air delivery conduit may be used. In order to provideimproved control of therapy delivered to the patient interface, it maybe advantageous to measure or estimate treatment parameters such aspressure in the mask, and vent flow. In systems using estimation oftreatment pressures, knowledge of exactly which mask is being used by aclinician can enhance therapy. For example, known flow generatorsinclude a menu system that allows the patient to select the type ofperipheral components being used, e.g., by brand, method of delivery,etc. Once the components are selected by a clinician, the flow generatorcan select appropriate operating parameters of the flow generator thatbest coordinate with the selected components.

The present technology may provide improvements to known apparatus tofacilitate the coordination between the flow generator and theperipheral components based on acoustic detection to distinguish oridentify particular components.

A first embodiment of the present technology comprises, a device, asystem, an identifier and/or a method for identifying the patientinterface device. The patient interface devices may be masks and thetubing for use with respiratory treatment apparatus such as a ContinuousPositive Air Pressure systems (“CPAP”) or similar systems. Thisembodiment may detect and identify the length of the tubing connected tosuch an apparatus or CPAP device, as well as the model of mask connectedto the tubing. The technology may also identify the mask and tubingregardless of whether a patient is wearing the mask portion at the timeof identification.

The technology may implement analysis of an acoustic signal sensed by amicrophone or other similar sensor near, proximal to or at a FlowGenerator (herein referred to as “FG”). However, is also possible toreplace the microphone with pressure or flow sensors.

This technology includes a proposed analysis method that enables theseparation of the response of the acoustic mask reflections from theother system noises and responses, including but not limited to motor orblower noises. This may make it possible to identify differences betweendifferent mask's acoustic reflections (usually dictated by mask shapes,configurations and materials) and may permit the identification ofdifferent masks without user or patient intervention.

An example method of detecting and identifying the mask may be tocompare a measured acoustic reflection response with a predefined orpredetermined database of previously measured reflection responses forknown masks. Optionally, some criteria would be set to determineappropriate similarity. In one example embodiment, the comparisons maybe completed based on the single largest data peak in thecross-correlation between the measured and stored reflection responsessuch as those represented by quefrency data determined from cepstrumanalysis. However, this may be improved by comparisons over several datapeaks or alternately, wherein the comparisons are completed on extractedunique sets of wave features.

Alternatively, the same measurement system or methodology may be alsoused to determine the tube or conduit length, by finding the delaybetween a sound being received from the FG and its reflection from themask; the delay may be proportional to the length of the tube.Additionally, changes in tubing diameter may increase or decrease theamplitude of the outputted waveforms and therefore may also bedetectable and identifiable. Such an assessment may be made bycomparison of current reflection data with prior reflection data. Thediameter change may be considered as a proportion of the change inamplitude from the waveforms (i.e., reflection data).

FIG. 13 depicts a schematic view of a further example embodiment of thepresent technology. The delivery tubing of the FG may be fitted with asmall microphone which records the sound pressure in the airpath. Themicrophone may be directly exposed to the airpath for greater receptionof noise or sound, or could also be encapsulated behind a thin layer offlexible membrane material. This membrane may function to protect themicrophone from heat and/or humidity.

In this example embodiment as shown in FIG. 13, the tube or tubing 13-1effectively acts as a wave guide for sound produced by the FG 13-4.Sound is emitted in this embodiment by the FG 13-4 and forms a firstsignal. The sound or first signal travels down or along the airpath intubing 13-1 to mask 13-2 and is reflected back along the tubing 13-1 byfeatures in the gas or airpath (which may include the tubing and/ormask) and is called the reflected second signal. A key feature of thetube response is the time required by sound to travel from one end ofthe system to the opposed end. This delay may mean that the microphone13-5 positioned at one end of the tube 13-1 receives the first signalcoming from the FG 13-4, and then some time latter receives the samesignal filtered by the tube 13-1 (reflected second signal), andreflected and filtered by the mask 13-2 (and potentially any othersystem attached, like human respiratory system, when the mask is fittedto a patient). This may mean that the part of the IRF associated withthe reflection from the end of the tube 13-1 appears after a delay. Thedelay may be equal to the time taken for sound to travel from the soundsource to the end of the tube, be reflected, and travel back again.

Another feature of the system IRF is that because the system isloss-prone, provided the tube is long enough, the part of the IRFassociated with the response at the FG will decay to a negligible amountby the time the mask reflection response has begun. If this is the case,then the mask response may be completely separated from the FG responsein the system IRF. As an example, FIG. 14 shows a sound measurement ofone such system IRF. Although of course in practice an imperfect impulseis used to excite the system, it still shows that the mask reflectionappears well separated from the FG response.

Generally, the time at which the mask reflection appears in the IRFshould be 2L/c plus any additional delay between the source and themicrophone (wherein ‘L’ is the length of the tube, and ‘c’ is the speedof sound in the tube). For practical purposes, we generally can ignorethe additional delay and approximate time zero when the microphone firstresponds to the impulse. This may generally allow the mask reflection tooccur at a time identified by 2L/c. Thus, the data associated with thetime of the mask reflection may be assessed in the identification ofwhich mask is connected to the flow generator by comparing this datawith known mask response data.

The example of IRF depicted in FIG. 14, illustrated the example systemexcited by an impulse which may be a noise including the motor/blower ofthe flow generator. Alternatively, it may be a sound impulse of shortduration from a speaker. In the methodology of this embodiment, anapparatus separates the reflected noise or sound signal (for example thenoise reflected from the mask or tubing conduits) from the other systemartifacts (including but not limited to the reflections from the FG),which may be implemented by analysis of the measured sound signal (e.g.,by examination of position and amplitude of data of either the soundsignal or the quefrency data from the cepstrum analysis associated withequations 1, 2, 3 and 4 previously described).

However, the generated noise may be either transient, or stationaryrandom. The latter case may make it more difficult to distinguish thereflection response from the system artifacts. Nevertheless, the presenttechnology may still serve to resolve the IRF despite the type ofimpulse.

Thus, in some embodiments, the generated noise or noise source can beproduced by a flow generator FG running at a constant speed so as toproduce the smeared clyclostaionary noise as previously discussed. Thus,the cepstrum analysis methodology may be implemented for separating themask reflection from this convolutive mixture. However, the accessoryidentification system may be able to determine and identify masks(and/or tubing) without separation of the noise and response.

FIG. 15 depicts various example cepstra from measurements of a flowgenerator FG system, such as the system of FIG. 13, that has been usedwith three different masks. Each respective mask in this example wastested at two different operational speeds of the flow generator, namely10 krpm, and 15 krpm. Although these speeds were used in the examples,the methodology may be implemented with other speeds particularly if thenoise generated and reflection is detectable by the microphone.

In the figure, the mask reflection can clearly be seen in all cases,beginning at around twelve milliseconds (12 ms). This time position iswhere it would be predicted since in the example system, a two metertube was used and the speed of sound is 343 m/s. In FIG. 15, the graphdepicts results from identifiable masks in the following order from topto bottom:

-   -   Ultra Mirage™ (shown in FIG. 18) using the flow generator FG at        10 krmp;    -   Ultra Mirage™, using the flow generator FG at 15 krmp;    -   Mirage Quattro™ (shown in FIG. 17) using the flow generator FG        at 10 krmp;    -   Mirage Quattro™ using the flow generator FG at 15 krmp;    -   Swift II™ (shown in FIG. 16) using the flow generator FG at 10        krmp; and    -   Swift II™ using the flow generator FG at 15 krmp.

By increasing the overall tubing length, an appreciable increase in thedelay of receiving the reflection from the mask may also be achieved.The increase in delay, when compared to FIG. 15, is in accordance withthe aforementioned calculations generating the approximations of thetubing length.

In embodiments of the technology, data associated with the mask and/ortube reflection, such as that centrally illustrated in the graph of FIG.15 may then be compared with similar data from previously identifiedmask (and/or tube) reflections such as that contained in a memory orDatabase of Mask Reflections.

For example, the “acoustic signature” of a tested mask may be separated,identified and filtered from the noisy measurement. This acousticreflection data may be compared to that of previous or predeterminedacoustic reflection data from known masks stored as a data template ofthe apparatus. One way of doing this is to calculate the crosscorrelation between the current measurement, and previously takenmeasurements for all known masks or data templates. There is a highprobability that the cross correlation with the highest peak shouldcorrespond to correct mask, and the location of the peak on the timeaxis should be proportional to the length of the tube.

However, more points of correlation may also increase the accuracy ofthe detection and identification steps of the present embodiment. Thus,additional data points may be utilized. Optionally, a least squaresalgorithm with the test data and known data sets may be implemented inthe mask/patient interface determination. Still further, in someembodiments additional feature extraction and recognition techniques maybe utilized, which may be based on artificial intelligence strategies.

As with previous embodiments, the methodologies or signal processingdescribed may be implemented by a controller or processor, such as withfirmware, hardware and/or software as previously discussed. Such acontroller may detect and/or identify the patient interface (which inthis one embodiment is a mask, tubing or a combination thereof). Thisidentification information or data relating to the presence or identityof the patient interface, may then be relayed to a further controller,processor, system or computer or used by the controller. The informationthen may be utilized in adjusting therapy or other settings for thecontrol of the flow generator in the delivery of therapy by therespiratory treatment apparatus.

For example, the aforementioned technology may be implemented as part ofa controller of a respiratory treatment apparatus such as a CPAPapparatus. Such an implementation may help to alleviate the need forusers or clinicians of the CPAP apparatus to manually input or adjustthe settings of the apparatus for use with particular patient interfacesor masks. Thus, some embodiments of the technology may even permit usersto change masks without user input or setup being required for the CPAPapparatus since such a system may automatically set the apparatus withsettings adjusted in accordance with the automatically identifiedpatient interface or mask configuration.

Additionally, in some embodiments, the information relating to theidentity or presence of the particular patient interface may beselectively sent or transmitted via the internet or some electronicmeans to a manufacturer, doctor or clinician so that the information maybe used to assist user's or patient's with troubleshooting patientinterfaces. For example, such data could be transmitted by wirelessinterfacing systems such as Bluetooth™ and/or Wi-fi™.

Alternately, in some embodiments of the present technology, a controllermay be utilized to detect whether the patient is currently wearing thepatient interface based on the nature of the acoustic reflection, forexample, by comparing test reflection data or cepstrum data to knownreflection data or cepstrum taken during patient use. Similarly, thetechnology may be implemented to determine whether there is a technicalproblem with the patient interface including leaks and/or kinks in thesystem. This may also be detected by comparing current test reflectiondata or cepstrum data to a known reflection data or cepstrum data takenwhile the patient interface was in good working order and properly on apatient (e.g., no leak).

In some embodiments, greater speeds may be implemented during reflectiontesting/measuring from the speeds illustrated above. For example, sometubing or conduits use materials with properties that may reduce noise.In such a system, the acoustic losses of the system may actuallyfluctuate. If the losses increase as detected by the measured signal(e.g., amplitude decrease), the decibels of the sound or noise sourcemay be increased to overcome the effects of sound loss. This may beachieved by increasing the speed of the flow generator during a testmeasurement. Additionally, other elements included in the air path ofthe mask or tubing may increase the acoustic losses. These elements mayinclude: humidifiers, noise baffles, and valves. Again, the lossattributable to these components may also be overcome by increasing thenoise source level or amplitude. Typically, a suitable noise or soundlevel from the sound source or flow generator may be about 20 dBa orgreater.

As previously mentioned, some embodiments may utilize a sound sourcesuch as a speaker to generate a sound pulse or white noise. This may beparticularly useful for respiratory treatment apparatus with very quietflow generators that do not generate much noise. For example, when usinga Resmed™ flow generator at speeds generally less than 6000 rpm, theflow generator is very quite. Under this condition, using the noise ofthe flow generator as an acoustic or noise emitter to produce theimpulse might be insufficient. This may be overcome by including anadditional noise emitter device either in the air conduit. This may beactivated during time periods of measurement such as when the patientinterface is initially attached to the flow generator. While a soundemitter might be a speaker, other emitters might be utilized. Forexample, a simple mechanical emitter might be implemented to vibrate inresponse to the flow of air from the flow generator such as a reed thatmay be selectively activated and deactivated (e.g., mechanically appliedand removed from the flow path of the flow generator or conduit.) Thismay then serve to selectively create the sound impulse. Alternatively,an actuated valve of the respiratory treatment apparatus may serve asthe sound source.

In some embodiments, the masks may be designed to have unique acousticsound response characteristics. For example, a unique sound resonatormay be designed within the mask or tubing to permit easierdifferentiation of the acoustic reflection signatures of each patientinterface.

In some embodiments, autocorrelation (i.e., the inverse FourierTransform of the power spectrum) may be implemented rather than usingcepstrum analysis.

In further embodiments of the present technology, acoustic reflectionsmay be analyzed to identify particular characteristics of patientinterfaces in addition to identification of type or model. For example,system response data may be utilized to identify characteristics ofpatient masks and conduit tubing. The characteristics may include:diameter, construction materials, volume of air cavities, overallconfigurations of the masks and/or tubing, etc.

Furthermore, as discussed in more detail herein, some embodiments mayalso detect and measure the reflections or echoes returned from thepatient's own respiratory system that is connected to the patientinterface. For example, the embodied systems, methods, and devices maydetect and identify the state, and conditioning of the patient'srespiratory system or even an identity thereof for authenticationpurposes. For example, the apparatus may be implemented to detect thediameter of airways of a patient at any given point or points. Using thesame or similar technique as previously discussed, closed airways orincreased resistance in the patient's airways may be detected.Furthermore, the embodiments may also be able to detect whether thepatient's mouth is open during CPAP treatment. Depending the problem orissue detected, the therapy may be adjusted accordingly. Such a systemmay also discern characteristics in the airpath between the flowgenerator FG and the patient's respiratory system (and including thepatient interface located in between) (e.g., leaks or blockage).Furthermore, while masks and tubes have been discussed for use with thetechnology, some embodiments may also be implemented with nasal prongs.Nasal prongs may present minimal interruption to the airpath and mayprovide clearer acoustic reflection data concerning the patient'srespiratory system.

For example, one or more leaks in the conduits of the respiratorytreatment apparatus (e.g., disconnection) or even past the mask, such asa mouth leak, may be detected from the acoustic reflection data orcepstrum data. For example, because cepstrum data may be understood toprovide information concerning the location of a noise reflection alonga sound conduit as discussed in more detail herein, the presenttechnology may permit an apparatus to discern the location of a leak inaddition to whether a leak has occurred. For example, the leak may beidentified and/or quantified by examination of cepstrum data such ascomparing known stored cepstrum data representative of a leak to acurrent test cepstrum data. Once the leak is identified from the cepstumdata, the location (e.g., based on the timing of the leak related datain the cepstrum data) may be detected and a suitable response by thedevice may be made. For example, a detected mouth-to-mask contact leak(mouth leak) might have a different automated apparatus response from aleak detected in a conduit or tube near the flow generator. Theapparatus might issue an audible warning for the latter while merelyincreasing the flow for the former.

Such a detector may be particularly suitable for high impedance tubing.For example, in 4 mm endotracheal ventilator tubing, the air passingthrough the tube generates a large amount of impedance. This impedancemeans that traditional methods of leak or accidental disconnectiondetection using pressure sensors or flow sensors struggle to identifythe leak. Thus, the present technology may detect leaks or disconnectionin low and high impedance tubing. Thus, such leak detection may also besuitable for implementation in high flow respiratory treatment devicesthat do not create pressurized therapy like CPAP devices. Such high flowdevices typically utilize nasal prongs that do not seal against theinner walls of the nose. These high flow systems typically include highimpedance conduits.

C. Patient/User Detection

Authentication or confirmation of a particular user of a particulardevice can have benefits. For example, limiting use of a medical deviceto a particular user may be significant for safety reasons. Consider arespiratory treatment device, such as a ventilator or continuouspositive airway pressure device.

A patient using such a device may require particular settings fortreatment, such as pressure delivery related settings. These settingsmay be prescribed by a physician. These settings may even beautomatically determined or refined over periods of treatment of thepatient with the device. Such settings may be only suitable for theparticular patient and not others. It may be appropriate to configuresuch a device so that it may detect when a particular user or patient isusing it or not so as to impede use by an unintended user.

It may be desirable for improved techniques and devices forauthenticating a user of the device to confirm that use is for aparticular person or impede use by others.

Accordingly, in the present technology automated devices provide methodsof bio-acoustic user authentication. In some embodiments, a sound sensordetermines a measure of sound of a sound generator within a soundconduit directed to an anatomical cavity of a user of a device. Themeasure of sound may be analyzed with a processor by calculation of acepstrum from the measure of sound. The processor may then determinethat the user is a pre-authorized user based on the analyzing. Incertain example embodiments, the technology may be implemented as asafety feature for operation of a respiratory treatment apparatus so asto confirm likely use by a particular user.

Thus, some embodiments of the present technology may involve methods anddevices for user authentication or user detection. In a typicalembodiment as illustrated in FIG. 19, the user detector apparatus 19-102will include a sound sensor 19-104, such as a microphone, and a soundauthentication controller 19-106. A sound conduit 19-108 in conjunctionwith a sound generator 19-110 can be configured to direct noise or soundto a user of a device with which the detector apparatus is combined. Forexample, the sound conduit 19-108 may be an endotracheal tube, CPAPmask, cannula, etc. in the case that the detector is combined with arespiratory treatment apparatus. Essentially, the conduit 19-108 assistsin directing sound or noise waves (waves illustrated as A1 in FIG. 19)to an anatomical cavity AC of a user. In a typical embodiment, the soundsensor 19-104 measures sound traversing within a conduit 19-108 foranalysis by the authentication controller 19-106. The sound may be thatgenerated by a sound source. For example, the sound may be thevibrations or noise created by the operation of a flow generator such asa servo-controlled blower in the case of a respiratory treatmentapparatus. For example, the flow generator may also be supplying a flowof breathable gas via the conduit 19-108 to the user's respiratorysystem. The measured sound can then include sound waves reflected fromone or more anatomical cavities of a user of the apparatus.

Such a sound signal from the sound sensor 19-104 can be sent to theauthentication controller 19-106. Optional analog-to-digital (A/D)converters/samplers (not shown separately) may be utilized in the eventthat supplied signal from the sensor is not in digital form and thecontroller is a digital controller. Based on the signal from the sensor,the controller assesses the sound signal to determine authenticationdata for comparison with previously determined authentication data.

In some embodiments, the authentication controller 106 may include aprocessor configured to implement particular detection methodologiessuch as the algorithms described in more detail herein. Thus, thecontroller may include integrated chips, a memory and/or other controlinstruction, data or information storage medium. For example, programmedinstructions encompassing such a detection methodology may be coded onintegrated chips in the memory of the device. Such instructions may alsoor alternatively be loaded as software or firmware using an appropriatedata storage medium. With such a controller or processor, the device canbe used for determining and analyzing sound data from the sound sensor.Thus, the processor may control the assessment for authentication oruser detection as described in the embodiments discussed in more detailherein.

One example of such a methodology or algorithm of the controller 19-106user detector apparatus 19-102 is illustrated in the flow chart of FIG.20. At 20-220, a sound sensor measures sound of a sound generator withina sound conduit directed to an anatomical cavity of a user of a device.At 20-222, the measure of sound from the sound sensor is analyzed by thecontroller or a processor thereof by calculation of a cepstrum from themeasure of sound. At 20-224, the controller or processor determines thatthe user is a pre-authorized user based on the analyzing. In such acase, the device may then permit operation or certain operations of thedevice. If it is determined that the user is not pre-authorized, certainoperations or all operations of the device may be prevented by theauthorization controller. Optionally, operations may be permitted orprevented by sending an enable or disable signal(s) from theauthorization controller 19-106 to another controller of the device.

In such embodiments of the technology, there may be a number of designconsiderations relating to the acoustic nature of the system and thedesire to isolate acoustic information associated with the user'sanatomical cavity for identification purposes. The components used todirect sound thereto can have many different configurations that mayyield different acoustic properties. One example conduit component maybe a tube having an approximately constant cross section along itslength “L”, with the user's anatomical cavity at a user end of theconduit (shown as “UE” in FIG. 19), and the sound generator (with otherpotential components) at the sound generator end (shown as “GE” in FIG.19). An acoustic characteristic of the conduit may be that it acts as awave guide for a wide range of frequencies along its length.

Thus, in some embodiments of the apparatus, detecting a user by theacoustic properties of the anatomical cavity might be based on theacoustic reflection in a conduit during sound generator operations bycomparing data measured during a setup procedure while being worn by auser and test data measured as a before a user attempts to begin orcontinue some operations of the device.

The Impulse Response Function (“IRF”) of the sound or noise in such asystem created by a sound generator as a sound source to a sound sensorcan contain a delta function at a chosen time zero, and a reflectionfrom the anatomical cavity at time greater than 2L/c, where the speed ofsound is denoted by “c”, and the length of the conduit is “L”. Such dataof the reflection may be recorded and stored during a set-up process forsubsequent analysis. Thereafter, if the user wishes to use the device,another IRF of the system may record a new reflection at time greaterthan 2L/c from the anatomical cavity for comparison with the originalset-up data. A significant change in the reflection may be indicative ofa different user whereas an unsubstantial difference or identity in thereflection data may be indicative of the same user from the set-upprocess.

As previously discussed, one potential method for monitoring for such achange or similarity in the reflection from the conduit may be based onthe calculation of a cepstrum of a signal from the sound sensor.

By comparing cepstrum data from the conduit and anatomical cavity of apre-authorized user with cepstrum data from the conduit and anatomicalcavity of a subsequent user, the comparison, such as differences therebetween, may be considered in identifying the user. For example, if acommon noise source is used in both tests, the comparison or differencebetween the cepstrum data of both tests may be considered an indicationthat the present user is not the same as the user of the set-up processor that the present user is the same as the user of the set-up process.

Some factors of the IRF (from the sound generator as a sound source tothe microphone response) are herein explained. When the conduit acts asa wave guide for sound produced by the sound generator, sound is emittedand forms a first signal (illustrated in FIG. 19 as “A1”). The sound orfirst signal travels down or along the conduit, which may or may nothave a significant length, to the anatomical cavity and is reflectedback along the conduit. The reflected sound may be considered a secondsignal (illustrated in FIG. 19 as “A2”). As previously mentioned, afeature of the example conduit response is the time required by sound totravel from one end of the system to the opposed end. This delay maymean that the sound sensor positioned at one end of the conduit receivesthe first signal coming from the sound generator, and then some timelater receives the same sound filtered by the conduit as reflectedsecond signal A2 (and potentially any other system attached, like humanrespiratory system). This may mean that the part of the IRF associatedwith the reflection from the anatomical cavity appears after a delay.The delay may be considered approximately equal to the time taken forsound to travel from the sound source to the anatomical cavity, bereflected, and travel back again.

When the system is loss-prone, given the length of the conduit, the partof the IRF associated with the response at the sound generator willdecay to a negligible amount by the time the reflection response hasbegun. When this occurs, the response due to the anatomical cavity maybe completely separated from the sound generator response in the systemIRF.

In the case that a sound source is a noise generator, the methodologyand system for separating the anatomical cavity reflection from theconvolutive mixture may be that as explained in previous embodiments.

Thus, in some embodiments, a continuous sound of sound generator may betaken as the sound impulse to the system by considering an arbitrarypoint in time during the continuous sound to be the sound impulse. Insuch a case, the sound generator would not need to produce periods ofsilence or reduced sound before and after a relative increase in soundto thereby produce an actual momentary sound impulse. However, in someembodiments such a momentary sound impulse may be generated bymodulation of the sound control signals to the sound generator or soundsource. For example, in the case of the flow generator serving as asound source a constant motor speed may be implemented to generate thenoise. Alternatively, modulation may be implemented by setting low or nospeed, followed by an instantaneous high speed and then followed by areturn to the low or no speed. Other methods of implementing a momentarysound impulse may also be implemented such as a speaker implementingchirp or other acoustic sound.

FIGS. 21 through 24 show hypothetical data graphs illustrating ananalysis of sound data from a microphone to detect a user by soundreflection associated with an anatomical cavity based on the previouslydescribed cepstrum methodology. In FIG. 21, data from two distinct soundmeasurement tests (e.g., a set-up process and subsequent userauthentication process) are plotted on a common axis. Sound can bemeasured such that samples of the microphone signal may be collected orrecorded from a chosen or controlled time zero until a sufficient periodof time has lapsed to permit the sound to traverse the conduit beyondthe user end, reflect from the anatomical cavity and return to themicrophone. In both cases illustrated in FIG. 21, the anatomical cavitysubjected to the measurement process was confirmed to be that of thesame user. The measurement samples or sound data from the microphone ineach test would be subjected to the operations described by equations 1,2, 3 and 4 previously mentioned. The result of this process isillustrated in the graphs.

In FIG. 22, the difference or magnitude of the difference from the dataof the two plots of FIG. 21 is shown. Such a difference or magnitude mayoptionally be determined on a sample-by-sample basis as the absolutevalue of the difference between the sound data of the two tests. Theapproximately flat line having no significant samples may be taken as arepresentation that the device has properly detected the user (or heranatomical cavity) who had been tested and associated with the device ina set-up process. In an example process, the samples of the differencedata may be evaluated by a threshold value to assess their significance.

In FIG. 22, hypothetical data from two distinct sound measurement testsare again plotted on a common axis. In one case the conduit subject tothe measurement process was coupled with an anatomical cavity of a firstuser (USER 1) and in the other case the conduit of the measurementprocess was coupled with an anatomical cavity of a second, differentuser (USER 2). The sound data from the microphone in each test can besubjected to the operations described by equations 1, 2, 3 and 4previously mentioned and then plotted. In FIG. 23, the difference ormagnitude of the difference from the data of the two plots can then bedetermined on a sample-by-sample basis and plotted. The presence of anysignificant difference in one or more samples (e.g., one or more valuesin excess of a threshold at a point along the plot) may berepresentative of an unauthorized user or user who did not participatedin the measurements of the set-up process. Such a determination may bemade by scanning and assessing the samples of the difference data.Optionally, data samples with information beyond the end of the conduitparticularly associated with the acoustic reflection of the anatomicalcavity may be the focus of the analysis. Such samples or data areillustrated in FIG. 24 with the reference character ACD. In this regard,given the known length of the conduit, the data of the plot beyond theconduit end may be assessed given that the cepstrum data is a functionof seconds as follows:T _(s)>=(2×L)/C

Where:

T_(s) is a time position of a sample in seconds; and

L is the known length of the conduit; and

C is the speed of sound.

It will be understood that this calculation may be adjusted to accountfor the distance from the microphone to the sound generator end of theconduit.

Based on the comparison between the cepstrum data determined in anauthentication test process and the cepstrum data previously determinedduring a set-up process, a device may be controlled so as to limit orpermit operations upon confirmation that examined cepstrum data issufficiently similar or not sufficiently similar. For example, based onthe cepstrum analysis, a detection of a significant value or values inthe cepstrum difference data may be taken as being indicative of adifferent anatomical cavity or a different user. In such a case, theauthentication controller may be implemented to disable (or enable) oneor more operations of the device that includes the user detectorapparatus 19-102 depending on the desired consequence of theauthentication results.

While this technology might be implemented as a method to uniquelyauthenticate a single person of a protected device, it is recognizedthat such a unique authentication of a particular user may not bestrictly necessary. Thus, benefits described herein may still beachieved if the implemented authentication system precludes some or mostother possible users while still allowing the originally verified userto use the protected device.

As previously mentioned, the user detector apparatus 19-102 may beconfigured with a pre-measuring set-up process to establish a basis forcepstrum data of an anatomical cavity of a verified, known or authorizeduser. For example, when the device is first operated the set-up processmay be triggered. Alternatively, data from such a process may bepre-stored into the user detector apparatus from another device. Whensubsequent authentication tests are made by the apparatus, such as in anautomatic start-up process, the subsequent test measurement data may beused for comparison with the pre-stored data to confirm use by theauthorized or intended user.

Optionally, in addition to or as an alternative to disabling the device,the detector may trigger a warning message and/or alarm to identify thatthe device is intended for a different user. Thus, a controller of suchan apparatus may optionally include a display device such as one or morewarning lights (e.g., one or more light emitting diodes). The displaydevice may also be implemented as a display screen such as an LCD.Similarly, detection of a different or unintended user may trigger a newinitialization of the particular device protected by the user detectionapparatus so that the operations of the device will commence withsettings suitable for all users rather than the settings that may havebeen particularly determined for a particular user.

As previously mentioned and illustrated in FIG. 25, an example detector25-702 of the present technology may be implemented as a respiratorytreatment apparatus. In such an embodiment, the sound sensor may beintegrated with a respiratory treatment apparatus conduit (e.g., anendotracheal tube or ventilator supply conduit) or installed in a partof a conduit coupler, mask or nasal cannula. In the example of FIG. 25,a microphone may be installed in a conduit that also serves to direct asupply of air to a patient's respiratory system such as the nares and/ormouth of a patient. In such a case, one or both nares and/or the mouthmay serve as the anatomical cavity or cavities for user detection.

In further reference to the example embodiment of FIG. 25, a controller25-706 that controls the delivery of pressure or ventilation treatmentof a patient via a flow generator, may also serve as the userauthentication or user detection controller. In such an embodiment, thesound sensor 25-704 may be directly coupled with the controller 25-706of the respiratory treatment apparatus for the acoustic measurements inthe conduit 25-708. Such a device may also include a pressure sensor,such as a pressure transducer to measure the pressure generated by theblower 25-710 and generate a pressure signal p(t) indicative of themeasurements of pressure. It may also optionally include a flow sensor.Based on flow f(t) and pressure p(t) signals, the controller 25-706 witha processor may generate blower control signals if such operations arepermitted or enabled by the methodology of the user detection controlleras previously discussed.

Thus, the controller may generate a desired pressure set point andservo-control the blower to meet the set point by comparing the setpoint with the measured condition of the pressure sensor. Thus, thecontroller 25-706 may make controlled changes to the pressure deliveredto the patient interface by the blower 25-710. Optionally, it mayinclude a speed sensor so as to control the blower to a particular RPMsetting. In this regard, in addition to automated respiratory treatment,the blower may serve as a source of noise or sound generator during theacoustic measuring as previously described such as by running the blowerat a constant speed during the time period of the sound sensor'smeasurement.

An example architecture for a user detection controller 26-806 isillustrated in the block diagram of FIG. 26. In the illustration, thecontroller may be implemented by one or more programmable processors26-808. The controller may also include a display interface 26-810 tooutput data for a user interface or display device as previouslydiscussed (e.g., warnings or messages, etc.) such as on a monitor, LCDpanel, touch screen, etc. A user control/input interface 26-812, forexample, for a keyboard, touch panel, control buttons, mouse etc. mayalso optionally be included for inputting data, or otherwise activatingor operating the methodologies described herein. The device may alsoinclude a sensor or data interface 26-814, such as a bus, forreceiving/transmitting data such as programming instructions, settingsdata, sound data, microphone sound samples, acoustic measurement data,cepstrum data, etc.

The controller also includes memory/data storage components 26-820containing control instructions and data of the aforementionedmethodologies. For example, at 26-822, they may include stored processorcontrol instructions for sound signal processing and userauthentication/detection processing, such as, measurement, filtering,FFT, logarithm, cepstrum comparison/assessment, difference determinationetc. At 26-824, these may also include stored processor controlinstructions for device activation or control, such as such as theinstructions for which operations are permitted or prevented inaccordance with the user detection/authentication. Finally, they mayalso include stored data at 26-826 for the methodologies such as soundmeasurements, cepstrum data, set-up data, thresholds etc.

In some embodiments, the processor control instructions and data forcontrolling the above described methodologies may be contained in acomputer readable recording medium as software for use by a generalpurpose computer so that the general purpose computer may serve as aspecific purpose computer according to any of the methodologiesdiscussed herein upon loading the software and data into the generalpurpose computer.

While the authentication or user detection technology has been describedin several embodiments, it is to be understood that these embodimentsare merely illustrative of the technology. Further modifications may bedevised within the spirit and scope of this description.

For example, while an integrated user authentication control device iscontemplated by the present technology, the methodology of thecomponents of the device may be shared across multiple components of asystem. For example, a measuring device may simply conduct the measuringprocesses to determine the acoustic data of the conduits and transferthe data to another processing system. The second processing system mayin turn analyze the data to determine the authentication as previouslydiscussed. The second processing system may then indicate theauthentication as described herein, such as by sending one or more ofthe described enabling or disabling messages, in electronic form forexample, back to the measuring apparatus or other device, for control ofthe operations of a device.

Similarly, while the technology contemplates embodiments where data fromonly a single microphone may be implemented to detect the user, in someembodiments of the technology additional microphones may be implemented.Moreover, while the technology contemplates embodiments where the noiseor sound of the system that serves as the sound impulse is the soundgenerated by a flow generator operating at one or more chosen blowersettings, in some embodiments, a speaker or horn driver may beimplemented in the conduit to generate the sound impulse that isrecorded by the sound sensor.

In some embodiments, the technology may also be implemented with a soundgenerator that is an audio device such as a digital audio file player,such as a hand-held device. The components of the audio device mayinclude a user detector as previously described and may be configured togenerate sound into auditory canal(s) using the conduit of an ear plugor ear speakers for authentication. Operation(s) of the audio device maythen be enabled or disabled based on a microphone of the ear plug or earspeakers and a processor of the audio device configured with the sounddetection methodologies as previously described.

D. Further Embodiments

Other variations can be made without departing with the spirit and scopeof the technology. For example, any of the described features of theaforementioned embodiments (e.g., obstruction detection features,component detection features and user detection features) may becombined together to form various additional apparatus so as to includethe benefits of the various functionalities disclosed.

In some embodiments, the acoustic detection methodologies may beimplemented without an automated sound source such as a sound speaker oran operating flow generator. For example, a user of a mask may create asound pulse by making a sound (e.g., humming) into the mask which maythen be measured by the apparatus for some of the detectionmethodologies previously mentioned. This sound reflection might then beused to identify the mask by the processing of the controller ofapparatus.

The invention claimed is:
 1. A device for use with a respiratorytreatment apparatus comprising a motor and impeller configured togenerate a supply of pressurized air from an outlet along an artificialairpath connected to a patient interface and thereafter into a patientfor treatment, the device comprising: a sensor configured to receive andtransduce (a) a first acoustic signal generated by operation of themotor and impeller, and (b) a second acoustic signal (i) representativeof the first acoustic signal having been reflected back from the patientinterface along the artificial airpath, and (ii) corresponding to thepatient interface; and a controller configured to: process thetransduced first acoustic signal and second acoustic signal, and detectthe patient interface connected to the artificial airpath based on theprocessed transduced second acoustic signal, wherein the controller isconfigured to identify, using the processed transduced second acousticsignal, a model of the patient interface so as to differentiate themodel of the patient interface from other patient interface models. 2.The device of claim 1, wherein the first acoustic signal is either anacoustic impulse or a continuous acoustic signal.
 3. The device of claim1, wherein the controller processes the transduced first and secondacoustic signals by cepstrum analysis.
 4. The device of claim 1, whereinthe sensor is one of the group consisting of: a microphone; a pressuresensor; and a flow sensor.
 5. The device of claim 1, wherein thecontroller is further configured to transfer information about theconnected patient interface to a computer.
 6. The device of claim 1,wherein the sensor is mounted or encapsulated in or positioned proximalto the outlet of the respiratory treatment apparatus.
 7. The device ofclaim 1 wherein the detecting further comprises (a) identifying a maskmodel, or (b) detecting a leak associated with the patient interface. 8.The device of claim 1 wherein to process and detect, the controller isconfigured to evaluate quefrency data.
 9. The device of claim 8 whereinto process and detect, the controller is configured to compare measuredand stored responses represented by the quefrency data.
 10. The deviceof claim 9 wherein to compare the measured and stored responses, thecontroller is configured to compute a cross-correlation of the measuredand stored responses, and detect one or more peaks in thecross-correlation.
 11. The device of claim 10 wherein the controller isconfigured to evaluate a time position of the detected one or morepeaks.
 12. The device of claim 9 wherein to compare the measured andstored responses, the controller is configured to perform a leastsquares operation with the measured and stored responses.
 13. The deviceof claim 1 wherein the controller is further configured to adjust asetting of the respiratory treatment apparatus based on the identifiedmodel of the patient interface.
 14. The device of claim 13 wherein inresponse to the detection, the controller selects (a) operatingparameters of a flow generator or (b) settings for control of a flowgenerator.
 15. The device of claim 1 wherein the controller isconfigured to operate the motor and impeller to produce the firstacoustic signal.
 16. A method for use with a respiratory treatmentapparatus comprising a motor and impeller configured to generate asupply of pressurized air from an outlet along an artificial airpathconnected to a patient interface and thereafter into a patient fortreatment, the method comprising: transducing, with a sensor, (a) afirst acoustic signal generated by operation of the motor and impeller,and (b) a second acoustic signal (i) representative of the firstacoustic signal having been reflected back from the patient interfacealong the artificial airpath, and (ii) corresponding to the patientinterface; and with a controller coupled with the sensor: processing thetransduced first acoustic signal and the second acoustic signal, anddetect the patient interface connected to the artificial airpath basedon the processed second acoustic signal, wherein the controlleridentifies, using the processed transduced second acoustic signal, amodel of the patient interface so as to differentiate the model of thepatient interface from other patient interface models.
 17. The method ofclaim 16, wherein the processing the transduced first and secondacoustic signals comprises performing a cepstrum analysis.
 18. Themethod of claim 16, wherein the sensor is one of the group consistingof: a microphone; a pressure sensor; and a flow sensor.
 19. The methodof claim 16, further comprising, with the controller, (a) transferringinformation about the connected patient interface to a computer; and/or(b) operating the motor and impeller to produce the first acousticsignal.
 20. The method of claim 16, wherein the sensor is mounted orencapsulated in or positioned proximal to the outlet of the respiratorytreatment apparatus.
 21. The method of claim 16 wherein the detectingfurther comprises (a) identifying a mask model, or (b) detecting a leakassociated with the patient interface.
 22. The method of claim 16 theprocessing comprises evaluating quefrency data.
 23. The method of claim22, wherein the processing comprises comparing measured and storedresponses represented by the quefrency data.
 24. The method of claim 23wherein the comparing the measured and stored responses, comprisescomputing a cross-correlation of the measured and stored responses, anddetecting one or more peaks in the cross-correlation.
 25. The method ofclaim 24 further comprising evaluating a time position of the detectedone or more peaks.
 26. The method of claim 23 wherein the comparing themeasured and stored responses comprises performing a least squaresoperation with the measured and stored responses.
 27. The method ofclaim 16 further comprising, by the controller, adjusting a setting ofthe respiratory treatment apparatus based on the identified model of thepatient interface.
 28. The method of claim 27 wherein in response to thedetection, the controller selects (a) operating parameters of a flowgenerator, or (b) settings for control of a flow generator.